class_docstr: Default reward scheme for the 2D cutting env.
  
  :param invalid_action_penalty: Negative reward assigned for an invalid cutting specification.
  :param raw_piece_usage_penalty: Negative reward assigned for starting a new raw inventory piece.
docstr: Assign rewards and penalties according to respective events.
  
  :param maze_state: Not used by this reward aggregator.
  :return: List of individual event rewards.
docstr_tok: ["Assign","rewards","and","penalties","according","to","respective","events",".",":","param","maze_state",":","Not","used","by","this","reward","aggregator",".",":","return",":","List","of","individual","event","rewards","."]
code: def summarize_reward(
      self, maze_state: Optional[MazeStateType] = None
  ) -> float:
      """Assign rewards and penalties according to respective events.
  
      :param maze_state: Not used by this reward aggregator.
      :return: List of individual event rewards.
      """
  
      rewards: List[float] = []
  
      # penalty for starting a new raw inventory piece
      for _ in self.query_events(InventoryEvents.piece_replenished):
          rewards.append(self.raw_piece_usage_penalty)
  
      # penalty for selecting an invalid piece for cutting
      for _ in self.query_events(CuttingEvents.invalid_piece_selected):
          rewards.append(self.invalid_action_penalty)
  
      # penalty for specifying invalid cutting parameters
      for _ in self.query_events(CuttingEvents.invalid_cut):
          rewards.append(self.invalid_action_penalty)
  
      return sum(rewards)
  
file_path: maze/tutorials/tutorial_maze_env/part05_reward/reward/default_reward.py
imports: from typing import List, Optional
  from maze.core.annotations import override
  from maze.core.env.maze_state import MazeStateType
  from maze.core.env.reward import RewardAggregatorInterface
  from ..env.events import CuttingEvents, InventoryEvents
label_desc: Use image node_id 3 for calling the DefaultRewardAggregator obj's underlying member method code with example usage: obj.summarize_reward(maze_state) and returns: sum